Section3.6-students_SP11

Section3.6-students_SP11 - Recap. Binomial distribution: 0...

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1 Binomial distribution: [] E X np ( ) (1 ) Var X np p  ( ; , ) B x n p 0 ( ; , ) x k b x n p 0 00 ) 0 1 x k n k k x n p p x n k xn       Recap…. Section 3.4-3.5 Hypergeometric Distribution: () r N r x n x P X x N n            , max{0, ( )}, ,min{ , } x n N r n r ( ) , where . ( ) 1 1 rr E X n np p NN Nn Var X n p p N     Negative Binomial Distribution: 1 ( ) ) 1 x r r x P X x p p r , x = r , r +1, … 2 ) ( ) ( ) r r p E X V X p p The Poisson Distribution How do you model P(X=x) for the following “arrival events”? X = # of cars entering a highway in 1 minute X = # of calls received in 30 sec at a telephone exchange X = # of customers arriving at a checkout counter in 2 min X = # of patients arriving at Emergency in 1 hour X = # of tornados observed in day (during “peak season”) Assumption: P(X= x ) = Section 3.6
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Section3.6-students_SP11 - Recap. Binomial distribution: 0...

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